基于Halcon的水平仪气泡自动分类系统研究
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摘要
为了实现生产过程中水平仪气泡实时快速分类而研发机器视觉水平仪气泡自动分类系统,这是水平仪生产机器换人的关键。研究了基于Halcon的图像处理与识别技术,结合Qt语言设计了自动分类系统软件。介绍了自动分类系统的硬件构成、工作原理,以及图像获取和识别水平仪气泡特定参数的方法。分析了自动分类过程中图像滤波、边缘检测、轮廓提取等图像处理技术,并进行了实验验证。结果表明,使用基于Halcon的机器视觉图像处理技术,能使水平仪的气泡分类速度更快、更准确,单张图像平均检测时间≤786 ms,长度检测平均误差≤0.02 mm,满足水平仪生产机器换人的需要。
The level bubble automatic classification system based on machine vision, which is developed for the sake of real-time rapid classification of level bubbles in the production process, is the key to machine substitution for level production. The study designed the software of automatic classification system with Qt language, on the basis of Halcon image processing and recognition technology. It introduced hardware composition, working principle, image acquisition, and method of identifying the specific parameters of the level bubble. It analyzed the image processing techniques such as image filtering, edge detection and contour extraction in the automatic classification process, and implemented experiments for verification. The results show that using Halcon-based machine vision and image processing technology can accelerate level bubble classification and make it more accurate. The average detection time of a single image is not more than 786 ms, and the average error of length detection is not more than 0.02 mm, which can meet the needs of machine substitution for level production.
The level bubble automatic classification system based on machine vision, which is developed for the sake of real-time rapid classification of level bubbles in the production process, is the key to machine substitution for level production. The study designed the software of automatic classification system with Qt language, on the basis of Halcon image processing and recognition technology. It introduced hardware composition, working principle, image acquisition, and method of identifying the specific parameters of the level bubble. It analyzed the image processing techniques such as image filtering, edge detection and contour extraction in the automatic classification process, and implemented experiments for verification. The results show that using Halcon-based machine vision and image processing technology can accelerate level bubble classification and make it more accurate. The average detection time of a single image is not more than 786 ms, and the average error of length detection is not more than 0.02 mm, which can meet the needs of machine substitution for level production.
引文
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[2] 马浩慧.水平仪的发展及应用[J].上海计量测试,2017,44(4):42.
[3] 胡润宇.水平仪检定装置的设计与研究[J].现代工业经济和信息化,2018(8):24.
[4] 刘玲.二维水准器的图像检测与水平仪开发[D].南京:东南大学,2017.
[5] 魏煜,朱善安.图像处理在水平尺标定系统中的应用[J].计算机应用研究,2004(12):168.
[6] 盛伟,WANG Q G,朱善安.基于Canny边缘检测和加权最小二乘法的气泡水平仪实时检测方法[J].机电工程,2016,33(10):1182.
[7] 阚仁峰,杨龙兴,南玉龙.基于HALCON图像处理的焊缝识别与提取方法的研究[J].物联网技术,2017,7(5):29.
[8] 张银苹,葛广英.基于HALCON的车牌识别研究[J].现代电子技术,2014,37(16):92.
[9] 王志中.基于栅格识别的测量系统快速标定[J].科技创新与应用,2014(29):1.
[10] 刘国华.HALCON数字图像处理[M].西安:西安电子科技大学出版社,2018:50.
[11] 黄明鑫,杨龙兴,庄立东,等.基于HALCON图像处理的粘连零件颗粒计数方法研究[J].机械设计与制造工程,2016,45(2):85.
[12] 段军,张博.改进的Canny算子边缘检测算法研究[J].软件导刊,2018,17(10):68.
[13] 陈艺峰.Halcon在工件二维尺寸检测上的应用[J].机电技术,2011,34(4):12.
[14] 许桢英,徐家祥,黄建斌,等.基于Halcon的电连接器壳体零件孔径快速精密测量[J].制造业自动化,2015,37(22):31.
[15] 左东祥,陈晓荣.基于HALCON的刹车片尺寸和表面缺陷检测系统[J].电子科技,2016,29(11):78.